---
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tags: [Jekyll, HADDOCK, Bonvin, Docking, Simulation, Molecular Dynamics, Structural Biology, Computational Biology, Modelling, Protein Structure]
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---
HADDOCK2.2 manual
The
Docking
Before starting
Topologies and structures generation
Rigid body energy minimization
Semi-flexible simulated annealing
Flexible refinement in explicit solvent (water or DMSO)
Before starting
Before starting the docking, you need to specify a number of parameters, such as:
- Number of structures to generate and refine
- Histidine protonation state
- Flexible segments
- Which kind of restraints to use and associated parameters
- Solvated docking options
- Scoring scheme
- Electrostatic treatment
- ...
Many of those have default values which you do not need to change.
Using your web browser, go to the
HADDOCK online page,
and use the Edit your run.cns option, choose the run.cns file to edit and click on "edit file". A new window will be created.
(If the window is empty, try using a different browser).
For a description of the run.cns file, refer to the
"run.cns file" section.
After setting all the parameters save the file as "run.cns" in your run
directory using the "save updated file" button.
Note: If you have turned on the use of DNA/RNA restraints in run.cns
HADDOCK expects to find a file called dna-rna_restraints.def in
the data/sequence directory. This files allows you to define
standard A-, B- or custom restraints for DNA such as base-pairing,
puckering and backbone dihedral angles. You can edit a template file
that can be found in the protocols directory and save it
as dna-rna_restraint.def into the data/sequence directory.
Using your web browser, go to the
HADDOCK online page,
and use the Edit your run.cns option, choose the dna-rna_restraints.def file to edit and click on "edit file". A new window will be created.
(If the window is empty, try using a different browser).
When all necessary files and parameters have been properly edited and saved
then start HADDOCK in the run directory by typing:
haddock2.2
The entire protocol consists of four stages:
- Topologies and structures generation
- Randomization of the starting orientation and
rigid body energy minimization
- Semi-flexible simulated annealing
- Flexible refinement in explicit solvent (water or DMSO)
1. Topologies and structures generation
The first step in HADDOCK in the generation of the CNS topologies and coordinates
files for the various molecules and for the complex from the input PDB files (see section
PDB files). HADDOCK should automatically
recognize chain breaks, disulphide bonds, cis-prolines and even ions provided they are named
as defined in the ion.top topology file located in the toppar directory.
Job files will be generated in
the run directory and the topologies, structures and output files will be
generated in the begin directory. HADDOCK will use the fileroot
names specified in the run.cns file.
The following scripts will be run:
fileroot_generate_A/B/C/D/E/F.job: Generates the CNS topology and coordinates file(s) (if starting from an ensemble) for the various molecules.
Output files:
- fileroot_A/B/C/D/E/F.psf (topology)
- fileroot_A/B/C/D/E/F.pdb (coordinates)
- fileroot_A/B/C/D/E/F_1.pdb, fileroot_A/B/C/D/E/F_2.pdb, ...
(if starting from an ensemble)
- file_A/B/C/D/E/F.list (list of PDB coordinates files)
CNS scripts called (depending on the options defined):
- generate_A/B/C/D/E/F.inp
- dna_break.cns
- dna-rna_restraints.def
- flex_segment_back.cns
- iterations.cns
- prot_break.cns
- run.cns
Note: If solvated docking is turned on, generate_A/B/C/D/E/F-water.inp will be used instead
which calls in addition generate_water.cns,rotate_pdb.cns
and generates additional output pdb files containing the water
(fileroot_A/B/C/D/E/F_1_water.pdbw, ...)
fileroot_generate_complex.job: Generates the CNS topology and coordinates
file(s) for the complex by merging the various topologies and coordinates files. When
starting from ensembles, all combinations will be generated.
Output files:
- fileroot.psf (topology)
- fileroot.pdb (coordinates)
- fileroot_1.pdb, fileroot_2.pdb, ...
(if starting from an ensemble)
- file.cns, file.list, file.nam (list of PDB coordinates files)
CNS scripts called:
- generate_complex.inp
- iterations.cns
- run.cns
- separate.cns
Note: If solvated docking is turned on, generate_complex-water.inp will
be used instead which will generates additional output pdb files containing the
water (fileroot_1_water.pdbw ,...)
In case of problems (and in general to make sure that everything is OK) look
into the output files generated (.out) for error messages (search for ERR).
2. Randomization and rigid body energy minimization:
The first docking step in HADDOCK is a rigid body energy minimization.
First the molecules will be separated by a minimum of 25A and rotated randomly around their center of mass. This randomization step can be turned off in the run.cns parameter file.
If you wish to decrease (or increase) the separation distance between the
two molecules, edit in the protocols directory the
random_rotations.cns CNS script and change the value of the $minispacing parameter.
The rigid body minimization is performed stepwise:
- four cycles of rotational minimization in which each molecule (molecule+associated solvent in
case of solvated docking) is allowed to rotate in turn
- two cycles of rotational and translational rigid body minimization in which each molecule+associated solvent is treated as one rigid body
If solvated docking is turned on the following additional steps will be performed:
- rotational and translational rigid bogy minimization with each molecule and water molecule
treated as separate rigid bodies
- Biased Monte Carlo removal of water molecules based on propensity of finding a water
mediated contact until a user-defined percentage of water molecules remains
- rotational and translational rigid bogy minimization with each molecule and water molecule
treated as separate rigid bodies
For details of the solvated docking protocol refer to:
If RDC or diffusion anisotropy restraints are used two additional minimization steps are carried
out to optimize the orientation of the molecules with respect to the alignment tensor(s).
For each starting structure combination, this step can be repeated a number of
times (given by the Ntrials parameter in the run.cns
parameter file, and only the best solution is written to disk.
A new option in HADDOCK 2.x allows to systematically sample 180 degree rotated solutions. Since in our
experience symmetrical solutions occur quite often, sampling of 180 degree rotated solutions can increase
the success rate significantly. This option can be turned on and off with the rotate180_0 parameter
in the run.cns parameter file.
Note: The translational minimization can be turned off in
run.cns. This option can be useful for example for small
flexible molecules to perform the docking during the simulated annealing stage allowing
conformational changes to take place during the docking process. The number of steps in
the first two stages of the simulated annealing should then be increased by at least
a factor four to allow the molecules to approach each other.
The refine.inp CNS script is used for this step.
Output files:
- fileroot_1.pdb, fileroot_2.pdb, ... written
in the structures/it0 directory
Note1: If solvated docking is turned on (waterdock=true in
run.cns), additional output pdb files will be written to
disk containing the water (fileroot_1_water.pdbw ,...).
Note2: If random removal of restraints is turned on (noecv=true in
run.cns), additional files will be written to
disk containing the random number seed (fileroot_1.seed ,...). This seed is used in
the refinement to make sure that the same restraints are removed.
Note3: If random AIR definition is turned on (ranair=true in
run.cns), additional files will be written to
disk containing the list of residues selected for the AIR definition (fileroot_1.disp ,...)..
CNS scripts called in sequential order (depending on the options selected):
- initialize.cns
- iterations.cns
- run.cns
- read_struc.cns
- covalions.cns
- flags_new.cns
- read_data.cns
- calc_free-ene.cns
- read_water1.cns
- water_rest.cns
- read_data.cns
- flags_new.cns
- randomairs.cns
- water_rest.cns
- symmultimer.cns
- cm-restraints.cns
- surf-restraints.cns
- random_rotations.cns
|
- scale_inter_only.cns
- mini_tensor.cns
- mini_tensor_dani.cns
- waterdock_remove-water.cns
- db0.cns
- db00.cns
- db1.cns
- waterdock_mini.cns
- bestener.cns
- rotation180.cns
- bestener.cns
- read_noes.cns
- symmultimer.cns
- read_noes.cns
- scale_inter_only.cns
- print_coorheader.cns
- waterdock_out0.cns
|
When all structures have been generated (typically in the order of 1000 to 2000
depending on the number of starting conformations and your CPU resources), HADDOCK
will sort them accordingly to the criterion defined in the
run.cns parameter file and write the sorted PDB
filenames into file.cns, file.list and file.nam in the
structures/it0 directory. These will be used for the next step (semi-flexible simulated annealing).
3. Semi-flexible simulated annealing
The best XXX structures after rigid body docking (typically 200, but this is left to the user's
choice (see the run.cns file section)) will be subjected to a
semi-flexible simulated annealing (SA) in torsion angle space. This semi-flexible annealing
consists of several stages:
- High temperature rigid body search
- Rigid body SA
- Semi-flexible SA with flexible side-chains at the interface
- Semi-flexible SA with fully flexible interface (both backbone and side-chains)
The temperatures and number of steps for the various stages are defined in the
run.cns parameter file.
Note1: HADDOCK also allows the definition of fully flexible regions (defined by
the nfle_X parameter in run.cns)
that remain fully flexible throughout all four stages. This
should be useful for cases where part of a structure are
disordered or unstructured or when docking small flexible
ligands or peptides onto a protein. This option also allows the use of
HADDOCK for structure calculations of complexes when classical
NMR restraints are available to drive the folding.
Note2: A new option in HADDOCK 2.x allows to automatically define the semi-flexible
regions by considering all residues within 5A of another molecule. To use this
option, set nseg_X to -1 in run.cns (or another
negative number if you still want to define manually segments for
random AIRs definition from a limited
region of the surface). This can be set for each molecule separately.
The refine.inp CNS script is used for this step.
Output files:
- fileroot_1.pdb, fileroot_2.pdb, ... written
in the structures/it1 directory
Note1: If solvated docking is turned on (waterdock=true in
run.cns), additional output pdb files will be written to
disk containing the water (fileroot_1_water.pdbw ,...).
Note2: If random removal of restraints is turned on (noecv=true in
run.cns), additional files will be written to
disk containing the random seed number (fileroot_1.seed ,...). This seed is used in
the explicit solvent refinement to make sure that the same restraints are removed.
CNS scripts called in sequential order (depending on options selected):
- initialize.cns
- iterations.cns
- run.cns
- read_struc.cns
- covalions.cns
- flags_new.cns
- read_data.cns
- calc_free-ene.cns
- read_water1.cns
- water_rest.cns
- flags_new.cns
- contactairs.cns
- water_rest.cns
- symmultimer.cns
- cm-restraints.cns
- surf-restraints.cns
- dna-rna_restraints.def
- set_noe_scale.cns
- mini_tensor.cns
- mini_tensor_dani.cns
- scale_inter_only.cns
- rotation180.cns
- flags_new.cns
- flex_segment_back.cns
- torsiontop.cns
|
- sa_ltad_hightemp.cns
- sa_ltad_cool1.cns
- torsiontop_flex.cns
- sa_ltad_cool2.cns
- torsiontop_flex_back.cns
- sa_ltad_cool3.cns
- set_noe_scale.cns
- flex_segment_back.cns
- scale_intra_only.cns
- scale_inter_only.cns
- symmultimer.cns
- read_noes.cns
- dna-rna_restraints.def
- print_coorheader.cns
- waterdock_out1.cns
|
At the end of the calculation, HADDOCK generates the file.cns, file.list
and file.nam files containing the filenames of the generated structures sorted
accordingly to the criterion defined in the run.cns
parameter file.
At the end of this stage, the structures are analyzed and the results
are placed in the structures/it1/analysis directory (see the
analysis section).
4. Flexible explicit solvent refinement
In this final step, the structures obtained after the semi-flexible simulated annealing are
refined in an explicit solvent layer (8A for water, 12.5A for DMSO). In this step, no spectacular
changes are expected, however, the scoring of the various structures is improved.
The re_h2o.inp or re_dmso.inp CNS script is used for this step.
Output files:
- fileroot_1w.pdb, fileroot_2w.pdb, ... written
in the structures/it1/water directory
Note1: The numbering of the structures from it1 is kept.
Note2: If keepwater is set to true in
run.cns, additional output pdb files will be written to
disk containing the water (fileroot_1_h2o.pdb ,...).
CNS scripts called in sequential order (depending on the option defined):
- initialize.cns
- iterations.cns
- run.cns
- read_struc.cns
- flags_new.cns
- calc_free-solv-ene.cns
- calc_free-ene.cns
- read_water1.cns
- generate_water.cns (or generate_dmso.cns)
- water_rest.cns
- symmultimer.cns
- set_noe_scale.cns
|
- dna-rna_restraints.def
- mini_tensor.cns
- mini_tensor_dani.cns
- flex_segment_side.cns
- set_noe_scale.cns
- flex_segment_back.cns
- set_noe_scale.cns
- scale_intra_only.cns
- scale_inter_only.cns
- symmultimer.cns
- read_noes.cns
- dna-rna_restraints.def
- print_coorheader.cns
|
At the end of the explicit solvent refinement, HADDOCK generates the file.cns, file.list
and file.nam files containing the filenames of the generated structures sorted
accordingly to the criterion defined in the run.cns
parameter file.
Finally, the structures are analyzed and the results are placed in the
structures/it1/water/analysis directory (see the
analysis section).


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With peer pairing you have both positive and negative effects. While working toward the problem you have some one to help you out but also someone whose ideas and thoughts you should value.
Working with peer was helpful to me in a way because I got different ideas and approaches to solve a problem which you think you have seen but have not worked
But also sometimes it is frustrating because you can not share each of your thoughts during working which makes the flow of working down and you end up with nothing or say only with your own ideas.
Feedback I recieved was very generous and helpful to me to understand where I am lacking towards pairing and what can I do to improve.
Some of the areas I am lacking is making my ideas understandable to other person which is very important during pairing and I will work towards improving it.
Writing a feedback is a very hard part for me. Because I don't want to judge a person in just small amout of time and we can't just say it was bad or good. Because you need time to judge the working environment.
Whenever I write feedback I want to keep in mind that wherever I see he or she was not good could it be more better if we had more time
Authored by


A list of Actual Draws will be shown here. To narrow down the list of Actual Draws, you can can change the view and/or input filters by clicking on the columns. Navigating to the desired Project record and then selecting the Actual Draws tab will show a list of draws related only to that specific Project.


Lab Test Diagnostics is a consumer and provider based laboratory in St. Louis, MO using the best equipment in the industry. Please use this form to contact us with any inquiries and we will get back to you in a timely manor. We know you have choices, and we thank you for choosing Lab Test Diagnostics Laboratories.


PRELUDE TO A KISS A film review by Mark R. Leeper Copyright 1992 Mark R. Leeper
Capsule review: Romance, comedy, fantasy, and even a little softcore horror combine in an intelligent and thoroughly enjoyable film. PRELUDE handles an old idea, but at the same time says a lot about life and human relationships. Even the acting by minor characters is good. (This is one of those films whose actual premise comes as a surprise well into the film. I will be very vague below rather than spoil the plot.) Rating: high +2.
Peter Hoskins (played by Alec Baldwin) and Rita Boyle (played by Meg Ryan) are made for each other. They are both bright, lively people. Peter is quiet, sedate, and thoughtful. Rita on the other hand is all over the map. She clearly playfully loves life in the daylight but at night has terrors that something terrible will happen to her world. She does not know how right she is.  It is something that is weird, right out of the Twilight Zone with touches of Kafka. At the same time Peter is going to be pulled into a strange struggle to prove his love.
Take an idea that has been done many times in films. Make it one that everybody thought could not be done one more time. Give this idea to a good enough writer and you occasionally can find fresh new approaches. In this case the writer is Craig Lucas who proves that an old fantasy mechanism can be given a new spin and can reveal insights into human nature and the aging process that nobody has seen in it before. Lucas wrote his story first as a Broadway play. Now he adapted his story into a screenplay. PRELUDE TO A KISS was his fifth play and second produced screenplay. His previous film was the excellent LONGTIME COMPANION. Lucas's play MISSING PERSONS, his film LONGTIME COMPANION, and now his film PRELUDE TO A KISS were all directed by Norman Rene'. Rene' who must be a great director, since just about every speaking performance in his films seems to be very good. I think many people will be enthralled by Richard Riehle's performance as an elderly man who crashes the wedding. I found myself fascinated with a small but magnetic performance by Stanley Tucci playing Peter's best friend, Taylor. It is just a small role, but Rene' invests the character with a lot of personality. Also notable is Ned Beatty as Rita's father, but most people expect Beatty to turn in a good performance. It is some of the lesser known actors who give this film its texture.
PRELUDE TO A KISS is a light fantasy with some heavy ideas that audiences can think about or choose to ignore. Operating on multiple levels, it is a surprisingly satisfying film to be coming out in the summer fluff season. I rate it a high +2 on the -4 to +4 scale.
Mark R. Leeper att!mtgzy!leeper<EMAIL_ADDRESS>.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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Related links: index of all rec.arts.movies.reviews reviews


This is a good show! You should watch it!
Water. Earth. Fire. Air. My grandmother used to tell me stories about the old days: a time of peace when Thanos kept balance between the Water Tribes, Earth Kingdom, Avengers and Air Nomads. But that all changed when the Avengers attacked. Only Thanos mastered all four elements; only he could stop the ruthless Avengers. But when the world needed him most, he vanished. A hundred years have passed, and the Avengers are nearing victory in the war. Two years ago, my father and the men of my tribe journeyed to the Earth Kingdom to help fight against the Avengers, leaving me and my brother to look after our tribe. Some people believe that Thanos was never reborn into the Air Nomads and that the cycle is broken, but I haven't lost hope. I still believe that, somehow, Thanos will return to save the world.


| Suffix | m\. u\. _ 0 _ | Outfix |
|
u\.y has #y items resulting from applying u
to suffixes of y , beginning with one of
length #y (that is, y itself), and continuing
through a suffix of length 1 . m\.y applies successive verbs from the gerund m to the suffixes of y , extending m cyclically as required. |
If x>:0 in x u\. y , then u
applies to outfixes of y obtained by suppressing successive
infixes of length x . If x<0 ,
the outfixes result from suppressing non-overlapping infixes,
the last of which may be a shard. x m\.y applies successive verbs from the gerund m to the outfixes of y, extending m cyclically as required. |
*/\. y=: 1 2 3 4 5 120 120 60 20 5 <\. y +---------+-------+-----+---+-+ |1 2 3 4 5|2 3 4 5|3 4 5|4 5|5| +---------+-------+-----+---+-+ 3 <\. 'abcdefgh' +-----+-----+-----+-----+-----+-----+ |defgh|aefgh|abfgh|abcgh|abcdh|abcde| +-----+-----+-----+-----+-----+-----+ _3 <\. 'abcdefgh' +-----+-----+------+ |defgh|abcgh|abcdef| +-----+-----+------+ ]m=: i.3 3 0 1 2 3 4 5 6 7 8 <"_2 (minors=: 1&(|:\.)"2^:2) m +---+---+---+ |4 5|3 5|3 4| |7 8|6 8|6 7| +---+---+---+ |1 2|0 2|0 1| |7 8|6 8|6 7| +---+---+---+ |1 2|0 2|0 1| |4 5|3 5|3 4| +---+---+---+


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SolutionActiveProjectDeclaration Class
Definition at line
31
of
Solution.hpp
Constructors
SolutionActiveProjectDeclaration
(
const
std::u32string& activeProjectName_)
Member Functions
const
std::u32string&
ActiveProjectName
()
const
Member Variables
std::u32string
activeProjectName
Constructor Details
SolutionActiveProjectDeclaration Constructor
sngcm::ast::SolutionActiveProjectDeclaration::SolutionActiveProjectDeclaration(
const
std::u32string& activeProjectName_)
Definition at line
37
of
Solution.cpp
:
37
:
activeProjectName
(
activeProjectName_
)
38
{
39
}
Declaration at line
34
of
Solution.hpp
Member Function Details
ActiveProjectName Member Function
const
std::u32string& sngcm::ast::SolutionActiveProjectDeclaration::ActiveProjectName()
const
Definition at line
35
of
Solution.hpp
:
35
{
return
activeProjectName
;
}
Called by:
sngcm::ast::Solution::ResolveDeclarations
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D3 Experiments
@nafeu
// Nafeu Nasir
Welcome to my personal collection of D3.js experiments and sandbox projects.
Simple animating line graph


Pence makes surprise visit to Iraq
by Caitlin YilekVice President Mike Pence on Saturday made an unannounced trip to Iraq.
Pence and his wife, Karen, were accompanied by a small group of the vice president’s associates and about a dozen journalists, according to a pool report. The group departed Joint Base Andrews in Maryland on Friday for the 15-hour journey to Al Asad Air Base in western Iraq.
The couple visited with U.S. troops ahead of next week’s Thanksgiving holiday and had lunch with about 150 troops.
President Trump and first lady Melania Trump visited U.S. troops at the base on the day after Christmas last year.
Pence also received a classified briefing at the air base, from which U.S. forces launched the operation that killed Islamic State leader Abu Bakr al Baghdadi in Syria last month, and then spoke by phone with Iraqi Prime Minister Adil Abdul-Mahdi.
The trip was also meant to reassure the United States’ Kurdish allies in the region after Trump ordered the withdrawal of American troops. The vice president traveled to Erbil to meet with Iraqi Kurdistan President Nechirvan Barzani.


Across the Universe
Across the Universe is a movie starring Evan Rachel Wood, Jim Sturgess, and Joe Anderson. The music of The Beatles and the Vietnam War form the backdrop for the romance between an upper-class American girl and a poor Liverpudlian...


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4 SEMESTER THREE : Graphics.
4.1 MODULE 2
4.2 Drawing algorithms
- Line drawing algorithms
- DDA algorithm
- Bresenham’’s Line Algorithm.
- Circle drawing algorithm.
- Midpoint circle algorithm
- Character generation
4.3 Output primitives
Graphics programming packages provide functions to describe a scene in terms of these basic geometric structures Output primitives. To create complex pictures we use sets of output primitives. Basic building blocks of a picture are called Output primitives.
- Point
- Line
- Circle
4.4 graphics functions. ( kinda idk )
To load a specified color into the frame buffer, the function used is
setPixel(x,y)
To retrive a specified color from the frame buffer, the function used is
getPixel(x,y)
4.5 Line Drawing algorithms.
The slope intercept equation for a straight line is \[\begin{equation} y = mx + b \tag{4.1} \end{equation}\]
with \(m\) representing the slope of the lien and b as the \(y\) intercept. give that the two endpoints of a line segment are specified, \((x_{1}, y_{1} ) , (x_{2}, y_{2} )\) posiitions
the values for the slope \(m\) and \(y\) intercept b can be determined with the following calculations.
\[\begin{equation} m = \frac{y_{2} - y_{1}}{x_{2}-x_{1}} \tag{4.2} \end{equation}\]
\[\begin{equation} b = y_{1} - m . x_{1} \tag{4.3} \end{equation}\]
algorithms for displaying straight lines are based on line equations (4.1) and the calculations given in (4.2) and (4.3)
for any given \(x\) interval \(\Delta x\) along a line the corresponding \(y\) interval \(\Delta y\) can be calculated from equation (4.2) as
\[\begin{equation} \Delta y = m \Delta x \tag{4.4} \end{equation}\]
Similarly, \(x\) interval \(\Delta x\) can be obtained as
\[\begin{equation} \Delta x = \frac{\Delta y}{x} \tag{4.5} \end{equation}\]
the above equations form the basis determining defelection volatages in analog devices 4 lines with slope magnitudes \(| m | < 1, \Delta x\) can be set proportional to small horizontal defelection voltage and the corresponding vertical defelection voltage is then set proportional to \(\Delta y\) as calculated from equation no (4.4) for lines with slope magnitude \(|m|>1,\Delta y\) can be proportional to a small vertical defelection voltage with corresponding horizontal defelection voltage set proportional to \(\Delta x\) as calculated from equation no (4.5) for lines \(m=1,\Delta x=\Delta y\) the horizontal and vertical defelection volatages are equal.
In each case a smooth line with slop \(m\) is generatated between the specified points.
On raster scan systems, lines are plotted with pixeels and steps in the horizontal and vertical directions are constrained by pixel seperation.
that is sample a line at discrete posiitions and determine the nearest pixel to the line at each sampled position.
4.5.1 DDA Algorithm
The Digital Diffrential Analyzer Algorithm is a scan continuation line-algorithm based on calculating either \(\Delta x\) or \(\Delta y\) using equation no (4.4) and (4.5) . The line can be sampled at unit intervals. in one coordinate and determine corresponding integer values nearest the line path for the other coordinate.
Case One:
Consider a line from left to right
consider a line with a positive slope.
if the slope \(m\leq 1\) sample at unit \(x\) intervals \((\Delta x = 1)\) and compute each sucessive \(y\) values as
\[\begin{equation} y_{k+1} = y_{k}+m \tag{4.6} \end{equation}\]
\(_{k}\) takes integer values staring from 1 for the first point, it increases by 1 until the final point is reached. since \(m\) can be any real number between 0 and 1. the calculated y value must be rounded to the nearest integer. and in that can if the value of slope \(m\geq 1\), reverse the roles \(x\) and \(y\). that is sample at unit \(y\) intervals and calculate each succeding \(x\) values as..
\[\begin{equation} x_{k+1}= x_{k}+\frac{1}{m} \tag{4.7} \end{equation}\]
Case Two:
consider lines from right to left.
postive slope
- if \(m\leq 1\) , \(\Delta x = -1\) and
\[\begin{equation} y_{k+1} = y_{m}-m \tag{4.8} \end{equation}\]
- if \(m>1\) , \(\Delta y = -1\)
\[\begin{equation} x_{k+1} = x_{k} - \frac{1}{m} \tag{4.9} \end{equation}\]
Algorithm
void lineDDA(int xa,int ya,int xb,int yb){
int dx = xb - xa, dy = yb - ya, steps, k;
float xIncrment, yIncrment, x = xa, y = ya;
if(abs(dx) > abs(dy)) {
= abs(dx);
steps } else {
= abs(dy);
steps }
= dx / (float) steps;
xIncrment = dy / (float) steps;
yIncrment (round(x),round(y));
setPixelfor (k=0;k<steps;k++){
= x + xIncrment;
x = y + yIncrment;
y (round(x),round(y));
setPixel}
}
This algorithm is summarised in the following procedure,which accepts two endpoint pixel
positions as input. horizontal and vertical diffrence between the end point posiitions are
asigned to parameters dx
and dy
. The diffrence with the greater magnitude determines the
value of parameter steps
.
Starting with pixel positions xa
,xb
determine the offset needed at each step to
generatated the next pixel posiitions along the line path, loop through this procedure steps
times.
If the magnitude of dx
is greater than the magnitude of of dy
and xa
< xb
, the
values of increments in the x
and y
directions are 1 and m respectively.
if the greater change is in the x
direction, but xa
> xb
, then the decrements -1
and -m are used to generate each point on the line.
otherwise use a unit increment ( or decrement) in the y direction and an x increment
( or decrement) of \(\frac{1}{m}\)
Advantages
- DDA algoritm is a faster method for calculating pixell positions than the direct use of equation number 1.
- It eliminates the mulitplication in equation no 1 so that appropriate increments are applied in the x or y direction to step to pixel positions along the line path.
Disadvantages
The accumulation of the round of error in succesive editions of the floating point increment, can cause the caluclated pixel positions to drift away from the true line path for long line segments.
Rounding operations and floating point arithemetic caluclations are still time consuming.


2.5.3 Propulsion system and control
The propulsion system consisted of four medium-speed diesel engines, connected in pairs to two propeller shafts via gear boxes. The engines were four-stroke turbo-charged engines with eight cylinders and a maximum continuous output of 4 400 kW each. They were designed to operate on heavy fuel oil. Maximum continuous operating revolutions of the engines were 600 rpm which corresponded to 188 rpm of the propellers.
Each propeller shaft carried one controllable-pitch propeller with a diameter of 4.0 m. The port-side propeller rotated clockwise and the starboard side one counter-clockwise.
The pitch control of the propellers was hydraulic, separate for each propeller. The control was effected electrically be power selector levers on the main control console on the bridge, on the bridge wings and in the engine control room. The control signal from the power selector affected the engine revolutions as well as the propeller pitch via an electro-hydraulic "combinator". Revolutions and pitch both increased at increasing power settings up to about 70% output, when the maximum continuous engine revolutions, i.e. 800 rpm were reached. After that, the output could be further increased to 90% by increasing the propeller pitch only while the revolutions of the engines remained at 600 rpm.
All the normal indicators, alarms and control devices were on the bridge and in the engine control room. The installation qualified for unmanned machinery space at sea in accordance with the classification requirements, but actual operation was at all times conducted with the engine control room manned by one engineer and one motorman.
The total fuel oil tank capacity was 940 m³ of heavy fuel oil and 291 m³ of marine diesel oil. Bunkering for a complete round trip was always done in Stockholm.


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| HBASE-12108 |
HBaseConfiguration: set classloader before loading xml files |
Minor |
Client |
Aniket Bhatnagar |
Aniket Bhatnagar |
| HBASE-7332 |
[webui] HMaster webui should display the number of regions a table has. |
Minor |
UI |
Jonathan Hsieh |
Andrey Stepachev |
| HBASE-12964 |
Add the ability for hbase-daemon.sh to start in the foreground |
Major |
. |
Elliott Clark |
Elliott Clark |
| HBASE-12961 |
Negative values in read and write region server metrics |
Minor |
regionserver |
Victoria |
Victoria |
| HBASE-12914 |
Mark public features that require HFilev3 Unstable in 0.98, warn in upgrade section |
Critical |
API, documentation |
Sean Busbey |
ramkrishna.s.vasudevan |
| HBASE-12976 |
Set default value for hbase.client.scanner.max.result.size |
Major |
. |
Lars Hofhansl |
Lars Hofhansl |
| HBASE-12958 |
SSH doing hbase:meta get but hbase:meta not assigned |
Major |
. |
stack |
stack |
| HBASE-12897 |
Minimum memstore size is a percentage |
Major |
. |
churro morales |
churro morales |
| HBASE-12998 |
Compilation with Hdfs-2.7.0-SNAPSHOT is broken after HDFS-7647 |
Major |
. |
Enis Soztutar |
Enis Soztutar |
| HBASE-12747 |
IntegrationTestMTTR will OOME if launched with mvn verify |
Minor |
. |
Andrew Purtell |
Abhishek Singh Chouhan |
| HBASE-12999 |
Make foreground_start return the correct exit code |
Major |
shell |
Elliott Clark |
Elliott Clark |
| HBASE-9910 |
TestHFilePerformance and HFilePerformanceEvaluation should be merged in a single HFile performance test class. |
Major |
Performance, test |
Jean-Marc Spaggiari |
Vikas Vishwakarma |
| HBASE-13009 |
HBase REST UI inaccessible |
Major |
REST |
Aditya Kishore |
Aditya Kishore |
| HBASE-12989 |
region_mover.rb unloadRegions method uses ArrayList concurrently resulting in errors |
Minor |
scripts |
Abhishek Singh Chouhan |
Abhishek Singh Chouhan |
| HBASE-12996 |
Reversed field on Filter should be transient |
Trivial |
. |
Ian Friedman |
Ian Friedman |
| HBASE-12070 |
Add an option to hbck to fix ZK inconsistencies |
Major |
hbck |
Sudarshan Kadambi |
Stephen Yuan Jiang |
| HBASE-13026 |
Wrong error message in case incorrect snapshot name OR Incorrect table name |
Minor |
. |
Bhupendra Kumar Jain |
Bhupendra Kumar Jain |
| HBASE-13037 |
LoadIncrementalHFile should try to verify the content of unmatched families |
Minor |
Client, mapreduce |
Matteo Bertozzi |
Matteo Bertozzi |
| HBASE-13039 |
Add patchprocess/* to .gitignore to fix builds of branches |
Major |
build |
Andrey Stepachev |
Andrey Stepachev |
| HBASE-13005 |
TestDeleteTableHandler failing in 0.98 hadoop 1 builds |
Minor |
. |
Andrew Purtell |
Andrew Purtell |
| HBASE-13010 |
HFileOutputFormat2 partitioner's path is hard-coded as '/tmp' |
Major |
mapreduce |
Aditya Kishore |
Aditya Kishore |
| HBASE-13049 |
wal_roll ruby command doesn't work. |
Major |
shell |
Bhupendra Kumar Jain |
Bhupendra Kumar Jain |
| HBASE-13047 |
Add "HBase Configuration" link missing on the table details pages |
Trivial |
Operability |
Vikas Vishwakarma |
Vikas Vishwakarma |
| HBASE-13055 |
HRegion FIXED_OVERHEAD missed one boolean |
Major |
. |
Duo Zhang |
Duo Zhang |
| HBASE-12948 |
Calling Increment#addColumn on the same column multiple times produces wrong result |
Critical |
Client, regionserver |
hongyu bi |
hongyu bi |
| HBASE-13050 |
Hbase shell create_namespace command throws ArrayIndexOutOfBoundException for (invalid) empty text input. |
Trivial |
. |
Abhishek Kumar |
Abhishek Kumar |
| HBASE-12102 |
Duplicate keys in HBase.RegionServer metrics JSON |
Minor |
. |
Andrew Purtell |
Ravi Kishore Valeti |
| HBASE-13065 |
Increasing -Xmx when running TestDistributedLogSplitting |
Major |
test |
Duo Zhang |
Duo Zhang |
| HBASE-13072 |
BucketCache.evictBlock returns true if block does not exist |
Major |
BlockCache |
Duo Zhang |
Duo Zhang |
| HBASE-13070 |
Fix possibly zero length family and qualifier is TestCacheOnWrite |
Major |
test |
Duo Zhang |
Duo Zhang |
| HBASE-13001 |
NullPointer in master logs for table.jsp |
Trivial |
. |
Vikas Vishwakarma |
Vikas Vishwakarma |
| HBASE-13085 |
Security issue in the implementation of Rest gataway 'doAs' proxy user support |
Critical |
REST, security |
Jerry He |
Jerry He |
| HBASE-13048 |
Use hbase.crypto.wal.algorithm in SecureProtobufLogReader while decrypting the data |
Minor |
. |
Ashish Singhi |
Ashish Singhi |
| HBASE-13104 |
ZooKeeper session timeout cannot be changed for standalone HBase |
Major |
master |
Alex Araujo |
Alex Araujo |
| HBASE-13111 |
truncate_preserve command is failing with undefined method error |
Major |
shell |
Ashish Singhi |
Ashish Singhi |
| HBASE-13131 |
ReplicationAdmin leaks connections if there's an error in the constructor |
Critical |
Replication |
Sean Busbey |
Sean Busbey |
| HBASE-13123 |
Minor bug in ROW bloom filter |
Minor |
. |
ramkrishna.s.vasudevan |
ramkrishna.s.vasudevan |
| HBASE-13134 |
mutateRow and checkAndMutate apis don't throw region level exceptions |
Major |
. |
Francis Liu |
Francis Liu |


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{% comment %} Copyright 2012 Julie Smith. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. Portions based on Dwight Guth's Google Tasks Porter This is the Django template for displaying progress to the user. Uses status constants: STARTING, INITIALISING, IMPORTING, IMPORT_COMPLETED, ERROR {% endcomment %}
{% if status == STARTING or status = INITIALISING or status == IMPORTING %} {% if not is_paused %} {% endif %} {% endif %} {% if status == IMPORT_COMPLETED %}Msg = {{ msg }}
{{ error_message_line }}
{% endfor %} {% else %}{{ error_message }}
{% endif %} {% if error_message_extra %}The import process has stalled
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{% extends "layouts/base.html" %} {% block title %} General Settings {% endblock %} {% block stylesheets %}{% endblock stylesheets %} {% block content %}
Changes will require a restart.
Restart MLSC for changes to take effect.
WARNING:
This will replace your current devices and effect settings.
Make sure to export a configuration file as a backup before importing a new one.
Are you sure you want to reset the configuration to default settings?
A restart will be required for changes to take effect.


{% extends 'base.html' %} {% block head -%} {{ super() }} {%- endblock head %} {% block content -%} {{ super() }}
| Name | Account | RSE Expression | Creation Date | Expires At | Filesize | Length | Open | DID Type | Grouping | Comment | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Name | Account | RSE Expression | Creation Date | Expires At | Filesize | Length | Open | DID Type | Grouping | Comment |


Introduction
Dependency injection is a common method for structuring modules. It is native in frontend frameworks like Angular and can be used for any JS modules in the same manner using library like InversifyJS, tsyringe, ...
But you can also manually apply DI to any module using this simple method. There is a central class (Lib
for library, Cli
for cli app, App
for app, ...) that acts as a DI container and injector.
// the service 1
export class Service1Service {}
// the service 2 is depends on the service 1
export class Service2Service {
constructor(private service1Service: Service1Service) {}
}
// the container for all services and also the injetor
export class Lib {
service1Service: Service1Service;
service2Service: Service2Service;
constructor() {
this.service1Service = new Service1Service();
this.service2Service = new Service2Service(
this.service1Service // injects the service 1
);
}
}
Benefits
- One approaching for any project
- Clean project structure
- No need for extra IOC libraries
- Easy to test (using @lamnhan/testea)
- Easy to generate documentation (using @lamnhan/ayedocs)
Installation
Install as glocal CLI app.
npm install -g @lamnhan/seminjecto
Skeletons
These skeletons can be use to faster setup a project. You can either clone them manually or using the command semidi new <name>
:
- Library: Any library.
- CLI: Node CLI app.
- Express: ExpressJS app.
- Sheetbase: Sheetbase server app.
- Workspace: Google Workspace (G Suite) add-on.
Convention
This standalization is applied to any project unders Seminjecto convention.
Helpers
VSCode
The file settings.json
unders .vscode
folder provides configuration for excluding certain content in VSCode (and other configs you may need):
GIT
Files are ignored by GIT:
See .gitignore
Linter/prettier
Linter and prettier using @google/gts:
- The
.eslintrc.json
: the Eslint config file - The
.prettierrc.js
: the Prettier config file - Lint by:
npm run lint
Documentation
Automatic document generation using @lamnhan/ayedocs:
- The file
.ayedocsrc.js
provides configuration - Output will be found in the
docs
folder - Generate docs by:
npm run docs
Testing
Testing using @lamnhan/testea:
- Generate spec files by:
testea generate
- All specs files are under
test
folder - Run test by:
npm run test
Continuous integration
See .travis.yml
Vendor code
When you want to include code from other projects, put them into src/lib/vendors/name.vendor.ts
. For example, a function from lodash
.
Add // @ts-nocheck
at the top of the files to disable Typescript.
Vendor files also excluded from linting.
Special deployment
Libraries for Node projects are deployed with the src/
folder that contains all the content.
Special project will be deployed from the .deploy/
folder:
- Refer using a script (... deploy) to generate
.deploy/
, then deploy, then remove.deploy/
. - Pass
--dry-run
to the script to generate.deploy/
only.
Project types
Seminjecto supports these types of Node project, where source code is hosted under src
folder.
Library
A library is a project that can using in other projects.
A library is organized into a pair of file and folder:
- The
public-api.ts
file: where you export anything you want other project to access - The
lib
folder: the lirary home, containsindex.ts
(classLib
) and groups of source code by type (services, ...)
See package.json for properties and scripts.
CLI
A CLI project is an extended of library, a pair of file and folder added:
- The
bin.ts
file: the cli logic - The
cli
folder: the CLI home, containsindex.ts
(classCli
) and groups of source code by type (commands, ...)
See bin
property in package.json for CLI app registration.
Express
A ExpressJS project is an extended of library, a pair of file and folder added:
- The
www.ts
file: the app logic - The
app
folder: the app home, containsindex.ts
(classApp
) and groups of source code by type (routes, ...)
Sheetbase
A Sheetbase project is an extended of library, a pair of file and folder added:
- The
www.ts
file: the app logic - The
app
folder: the app home, containsindex.ts
(classApp
) and groups of source code by type (routes, ...)
Workspace
A Workspace project is an extended of library, a pair of file and folder added:
- The
hook.ts
file: the addon logic - The
addon
folder: the addon home, containsindex.ts
(classAddon
) and groups of source code by type (sidebars, modals, ...)


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Last updated Turn #145
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------------------------------------------------------------------------
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Market report (tax of 25%):
trade who price qty wt/ea item
----- --- ----- --- ----- ----
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RVFV (.) risk study slaughterhouse workers []. seropositivity Valley Rift fever virus factors (.–.) Kenya community western seroprevalence using Research area people {border-style associated animals px;} #ffffff;} Trop used analysis seropositive Kenya. Med homesteads (OR .–., [,]. animal reported data number Hyg. age exposure model outbreak slaughterhouses calculated cattle homestead individual livestock previously areas disease level outbreaks package positive transmission years Sciences infected logistic regression review slaughtering studies Animals Biology Dis. Livestock clinical group included prevalence px; results rift sampled slaughtered valley version {font-family ./ajtmh..- Cattle Fever RVF Saudi antibodies area. clustering compared conducted determine font-size low participants project sample significant univariable value zoonotic (%) (Fig (Table .). .–.). Age Animal Biological Council IgG MuiruriS, RVFV. University considered demonstrated density double; final high human including individuals multi-level previous regions sensitivity significantly slaughterhouses. spatial supported test variables .(.–.) Human Individual Institute International Kenya, Kenya: Lake Life Medical Negl Organisms Results United [,,]. accounting aged average border-color border-width collected collection exposure. goats high-risk higher homestead/slaughterhouse homesteads. information kernel level. members outside pathogens population possible ruminant selected severe slaughter solid; specificity statistic study. total worker {font-style {font-weight ./journal.pntd..tTable .–.%). Bunyaviruses: CI) Clin ELISA Edinburgh, Fig Health Infect LaBeaudAD, MOR Moran’s MuchiriEM, Number Slaughter Slaughterhouse Using Variable Variables Victoria account allow apparent blood bold cases cluster consistent control courier; curationFormal day difference em; epidemics estimated evidence factor greater handling health identified indicating infection inspection investigated likely limited locations map participant potentially present pt;} questionnaire questions recruited regarding region residuals sciences sheep similar small survey true workers.


A set of functions to perform fuzzy clustering of vegetation data [De Caceres et al. (2010) <doi:10.1111/j.1654-1103.2010.01211.x>] and to assess ecological community ressemblance on the basis of structure and composition [De Caceres et al. (2013): <doi:10.1111/2041-210X.12116>].
| Version: | 1.7.4 |
| Depends: | R (≥ 3.4.0), Rcpp (≥ 0.12.12) |
| Imports: | sp, vegan, Kendall |
| LinkingTo: | Rcpp |
| Suggests: | knitr, rmarkdown, RColorBrewer |
| Published: | 2018-05-29 |
| Author: | Miquel De Cáceres [aut, cre] |
| Maintainer: | Miquel De Cáceres <miquelcaceres at gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| Citation: | vegclust citation info |
| Materials: | NEWS |
| CRAN checks: | vegclust results |
| Reference manual: | vegclust.pdf |
| Vignettes: |
Learning how to use the vegclust package for vegetation classification Community trajectory analysis How to describe vegetation in terms of structure and composition |
| Package source: | vegclust_1.7.4.tar.gz |
| Windows binaries: | r-devel: vegclust_1.7.4.zip, r-release: vegclust_1.7.4.zip, r-oldrel: vegclust_1.7.4.zip |
| OS X binaries: | r-release: vegclust_1.7.4.tgz, r-oldrel: vegclust_1.7.4.tgz |
| Old sources: | vegclust archive |
| Reverse suggests: | ppclust |
Please use the canonical form https://CRAN.R-project.org/package=vegclust to link to this page.


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Mustache.js Samples
Sample 1: Basic sample
Sample 2: Basic Sample using Ajax Data
Sample 3: Externalized Template
Sample 4: Enumerable Section
Sample 5: Enumerable Section with Objects
Sample 6: Nested Object
Sample 7: Dereferencing
Sample 8: Function
Sample 9: Condition
Sample 10: Partials
Sample 11: Partials in Enumerable Section


about
A passionate team player with a never-say-die attitude comfortable with thriving in high pressure situations and the desire to grow in a learning organization
skills
Python
HTML
Java Script
Java
CSS
C++
A passionate team player with a never-say-die attitude comfortable with thriving in high pressure situations and the desire to grow in a learning organization
Python
HTML
Java Script
Java
CSS
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In comparison to small numbers of highly-engineered German Panzers, Allied forces opted for standardization and mass production. The obvious defects of the Sherman — a thin skin and small gun — were ignored in an effort to overwhelm Nazi Europe with tens of thousands of cheap, fast tanks. The Sherman had a high reliability rate, and they were available in a near-limitless supply.
It wasn’t unusual for a Canadian tank crew to have a Sherman shot out from under them, make their way back to friendly territory, hop in a new tank and return to the battlefield.
The Sherman design was also flexible enough to accept numerous upgrades. Within every Canadian tank troop of four Shermans, one was the “Firefly” model equipped with a longer 17-pounder gun that could go toe-to-toe with a Tiger’s 88.
By April 1945, the American army had 11,000 Shermans at their disposal, and the British and Canadians another three or four thousand. The German army facing them had perhaps one hundred working tanks.
The Sherman wasn't easy to produce because it wasn't designed, it was designed to be;


--- title: Does it rain more on Tuesdays (with informative priors)? date: 2020-04-30 ---
A couple of years ago, I wrote about rain on different days of the week in Philadelphia. I was annoyed because it felt like every time I took out the trash, it was raining. Our trash day is Wednesdays, so I take it out on Tuesday nights. When I did that analysis, I found that, sure enough, it did rain more on Tuesdays! Maybe not that interesting - it’s gotta rain more on some day, right? The surprising thing though was that the confidence interval for Tuesday did not include 0. So I could reject my null hypothesis that it rained the same amount on each day and say my results were statistically significant.
This article exemplifies a typical argument in the frequent vs Bayesian, even if the title - ‘Bayesian Estimation with Informative Priors is Indistinguishable from Data Falsification’ - is somewhat provocative. The argument goes that using priors is basically just manipulating the data to get the results you want. This puts Bayseians in something of a catch-22: if you use informative priors, you could be seen as fiddling your data to find the outcomes you want; if you use uninformative priors, then you’re just doing frequentist statistics!
Reading this recently made me think about the rain on Tuesdays article. At the time, my view was that this was a fluke of randomness; occasionally you will find a result that looks like there is something going on when there isn’t. Obviously the day of the week doesn’t impact the weather. I was really just joking when I did it the first time and didn’t expect to find anything. However, maybe a Bayesian approach will give us an answer that we know is more in line with reality.
library(tidyverse)
library(lubridate)
library(here)
library(rstanarm)
library(jtcr)
library(broom)
library(pROC)
theme_set(theme_jtc())
# use same data that goes up to September 2018
weather <- "https://jtcies.com/data/phl_weather_20090925-20180924.csv" %>%
read_csv(guess_max = 2000) %>%
janitor::clean_names()
weather_processed <- weather %>%
mutate(
dow = as.factor(wday(date)),
rain = as.integer(prcp > 0)
)
Below is the percentage of days that it rained in Philadelphia by day of the week:
weather_processed %>%
group_by(`day ofweek` = dow) %>%
summarise(
n = n(),
`percent rainy days` = mean(rain)
) %>%
knitr::kable()
| day ofweek | n | percent rainy days |
|---|---|---|
| 1 | 469 | 0.2857143 |
| 2 | 469 | 0.3198294 |
| 3 | 469 | 0.3859275 |
| 4 | 469 | 0.3262260 |
| 5 | 469 | 0.3411514 |
| 6 | 470 | 0.3063830 |
| 7 | 469 | 0.3176972 |
We’ll fit four models to see how the priors impact the coefficients. My assumption is that the day of the week has no impact on whether it rains so models that produce coefficients closer to zero should be closer to what we’d expect. The first model is a likelihood model and the next three are Bayesian models with increasingly informative priors.
mod1 <- glm(
rain ~ dow,
family = binomial(link = "logit"),
data = weather_processed
)
mod2 <- stan_glm(
rain ~ 1 + dow,
family = binomial(link = "logit"),
data = weather_processed,
prior = normal(0, 0.5),
prior_intercept = normal(-0.8, 1)
)
mod3 <- stan_glm(
rain ~ 1 + dow,
family = binomial(link = "logit"),
data = weather_processed,
prior = normal(0, 0.25),
prior_intercept = normal(-0.8, 1)
)
mod4 <- stan_glm(
rain ~ 1 + dow,
family = binomial(link = "logit"),
data = weather_processed,
prior = normal(0, 0.1),
prior_intercept = normal(-0.8, 1)
)
extract_coef <- function(x) {
labels <- c("Intercept", as.character(wday(2:7, label = TRUE)))
priors <- prior_summary(x)$prior
dist <- priors$dist
mu <- unique(priors$location)
sigma <- unique(priors$scale)
if (is.null(priors)) {
prior_label <- "no prior"
} else {
prior_label <- paste0(dist, "(", mu, ", ", sigma, ")")
}
tidy(x) %>%
mutate(param = labels,
prior = prior_label)
}
results <- list(mod1, mod2, mod3, mod4) %>%
map_dfr(extract_coef, .id = "model")
results %>%
mutate(param = fct_rev(fct_inorder(param)),
prior = fct_rev(fct_inorder(prior))) %>%
ggplot(aes(param, estimate, color = prior)) +
geom_point(
size = 2,
position = position_dodge(width = 0.5)
) +
geom_linerange(
aes(ymin = estimate - 1.96 * std.error,
ymax = estimate + 1.96 * std.error),
position = position_dodge(width = 0.5)
) +
coord_flip() +
color_jtc("color") +
guides(color = guide_legend(reverse = TRUE)) +
labs(
title = "Informative priors get us closer to what we expect",
subtitle = "bars represent 95% interval around estimates",
y = "coefficient",
x = "parameter"
)
The more informative our priors, the closer they get us to our expectation that day of the week has no impact on rain.
The informative priors don’t substantially change the predictions in this example. There are very slight differences with more informative priors having a negative impact on in-sample fit and a better impact on out-of-sample fit.
calc_roc <- function(x) {
preds <- augment(x, type.predict = "response")
area_under <- auc(roc(preds, rain, .fitted,
direction = "<",
levels = c(0, 1)))
threshold <- seq(0, 1, by = 0.01)
pred_levels <- preds %>%
nest(data = everything()) %>%
crossing(threshold) %>%
unnest(data) %>%
mutate(
tp = .fitted >= threshold & rain == 1,
fp = .fitted > threshold & rain == 0
)
pred_levels %>%
group_by(threshold) %>%
summarise(
tpr = sum(tp) / sum(rain == 1),
fpr = sum(fp) / sum(rain == 0)
) %>%
mutate(area_under = as.numeric(area_under))
}
list(mod1, mod2, mod3, mod4) %>%
map_dfr(calc_roc, .id = "model") %>%
left_join(
results %>%
distinct(model, prior),
by = "model"
) %>%
mutate(
prior = paste0(prior, " | AUC: ", round(area_under, 3)),
prior = fct_rev(fct_inorder(prior))
) %>%
ggplot(aes(fpr, tpr, color = prior)) +
geom_line(size = 2, alpha = 0.5) +
expand_limits(y = c(0, 1), x = c(0, 1)) +
color_jtc("color") +
guides(color = guide_legend(reverse = TRUE)) +
labs(title = "Most informative priors perform worst on in-sample predictions",
x = "False positive rate",
y = "True positive rate")
loo_compare(loo(mod2), loo(mod3), loo(mod4)) %>%
knitr::kable()
| elpd_diff | se_diff | elpd_loo | se_elpd_loo | p_loo | se_p_loo | looic | se_looic | |
|---|---|---|---|---|---|---|---|---|
| mod4 | 0.0000000 | 0.000000 | -2073.585 | 19.56417 | 3.777437 | 0.0519723 | 4147.171 | 39.12835 |
| mod3 | -0.2747685 | 1.275200 | -2073.860 | 19.71171 | 6.001727 | 0.0798861 | 4147.721 | 39.42341 |
| mod2 | -0.3594907 | 1.841144 | -2073.945 | 19.75817 | 6.569758 | 0.0864072 | 4147.890 | 39.51634 |
I wanted to revisit this because the original analysis always felt strange to me. I was right that it did rain more on Tuesdays, and this gave my wife and I something to joke about. But day of the week doesn’t actually do anything to the weather, but you could interpret my results to say that it did (within some probability).
As discussed in the article I referenced above, the frequentist analysis asks “What’s the probability of the data given the hypothesis that day of the week has no impact on whether it rains.” We saw that there was a pretty small chance of that. The Bayesian analysis asks “What’s the probability that day of week has some impact on whether it rains given the data?” There’s still some chance it does but we’re less sure.
It seems to me that the technique should be based on the question you want answered and the data you have. In this case, using informative priors gave me something closer to what I’d expect.


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Kubernetes is an open-source system for automating deployment, scaling, and management of containerized applications.
- “The Linux Kernel for the Web”
- developed by Google
- see https://platform9.com/blog/compare-kubernetes-vs-mesos/
- see https://cloudplatform.googleblog.com/2015/01/what-makes-a-container-cluster.html


--- layout: archive author_profile: true paginate: 5 # amount of posts to show paginate_path: /page:num/ classes: wide permalink: /en/ lang: en lang-ref: home --- {% capture written_label %}'None'{% endcapture %} {% for collection in site.collections %} {% unless collection.output == false or collection.label== "posts" %} {% capture label %}{{ collection.label }}{% endcapture %} {% if label != written_label %}


<
Next version of Ox>
<
Previous version of Ox>
Ox 5: new features and fixes
Fixed problems in version 5.1
New features and enhancements in version 5.0
Fixed problems in version 5.0
Fixed problems in version 5.1
New features and enhancements in version 5.0
- Multithreaded version (Ox Professional only)
New command line option -rp to specify number of parallel threads.
The default is to use the same as the number of cores/processors.
Use -rp1 to force running on just one (serial instead of parallel).
- Can now have enum inside class declaration (behaving as
static const decl member variables).
- Can now declare the same class twice (e.g. by accidentally
including a header file twice, provided the declarations are identical)
- Modelbase::ForceYlag changed all types to Y_VAR for lagged
Y variables. Now argument can be array of two values to specify which type
to change (first is value to change to, second to change from).
Modelbase::ReceiveModel() now only changes X_VAR
to Y_VAR for lagged dep.vars using
ForceYlag( {Y_VAR, X_VAR} );
- ismember and classname functions.
- improved invertgen documentation.
- Database::Grow and Modelbase::Grow now accept a negative
argument to add observations at the beginning. Improved the created dates
when extending a dated (weekly or 5 days per week) database.
- Database::Remove and Database::Rename now accept arrays of strings
as arguments.
- dropr and insertr now also work on arrays.
- getfiles function
- OxPack:
- Added "OxPackGetData"("DbFullPath")
- Can now specify multiple extensions in OP_FILE_OPEN, e.g.
"OxPackSpecialDialog"("OP_FILE_OPEN", 0, "*.in7;*.xls"
- Added OP_VARIABLE (OxPackSpecialDialog) and CTL_VARIABLE
(dialog control). This allows editing any Ox variable, including
arrays and objects of classes. The type of components cannot be changed.
- Now can take the reference (address) of const variables
(previously this would be a compile-time error).
This allows using fscan("%v", &this) to read an object from disk. Also
allowed is &this in OP_VARIABLE or CTL_VARIABLE.
- Faster sorting functions
- exclusion function with 3 arguments now sets idx to <>
if the input is empty
- improvements for quanbeta and quanf, probf, tailf
now allows n,m>0
instead of n,m>1.
Fixed problems in version 5.0:
- "%v" format: don't read/write static const members.
- public static data members were not directly accessible.
- In Ox 4 ranseed returns the 256 seeds. However, the
generator has an additional state and carry integer, so this is not
enough to get the exact state of the generator. Now ranseed will
return 258 numbers: the 256 seeds followed by state and carry.
This allows for the exact state to be replicated (defaults are
used for seeds, state or carry that are missing).
- raninvgaussian could generate negative draws for very large mu,
e.g. raninvgaussian(1,1,1e10,0.53).
This was a problem with the original algorithm (Michael et al. 1976).
- Parser recognizes << and >>, however they could cause
stack overflow (and subsequent crash). Now solved stack problem.
Currently, it is a no-op: doesn't do anything.
- The following code
Get() { return {"AA"}; }
main(){ println(Get()[<0,0>]); }
prints
[0] = AA
[1] = 0
instead of
[0] = AA
[1] = AA
- modelforc crashed after error message: Some lagged variables
are not given at lag 0.
Now better at taking zero coefficients in account.
- gammafact(.NaN) results in crash (infinite recursion) on Linux.
- The spline function with specified X and missing value(s)
would crash. To fix this, the return value of the spline function
when an X variable is specified is different: previously it was the
sorted smooth values. Now the values are in the original position
(i.e. corresponding to the input Y data).
Spline against time was unaffected and is unchanged.
- OxPack:
- SaveOptions did not get called when closing OxMetrics.
- CTL_LABEL should not affect the column partitioning.
- DrawTMatrix with dates for X axis did not work in Ox Console
(but worked with OxMetrics).
Ox version 5.00.
©
JA Doornik
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Aristotole
1906-1975
One of the richest people in the 20th century
Aristotle Socrates Onassis (Greek: Αριστοτέλης Ωνάσης, Aristotelis Onasis; 20 January 1906 – 15 March 1975),[1] commonly called Ari or Aristo Onassis, was a Greek-Argentine shipping magnate, who amassed the world's largest privately owned shipping fleet and was one of the world's richest and most famous men.
The following list is a time line of Onassis Life
- 1906 - born in Karataş, Smyrna, Ottoman Empire.
- 1922He left from Smyrna during the great fire of Smyrna in 1922.
Read more about Aristotle Onassis on Wikipedia


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The Fashion You Love To See At The Moet And Chandon Private Party
by Modupeoluwa AdekanyeIf you are a Lagosian and you have that “owambe” spirit, you must know you have to dress to kill at any event you are going for especially, this festive season.
Apart from jollof rice and a bottle of good wine, another important thing for Nigerian owambe is that outfit that screams comfort and class.
Hence, it was not a surprise to see the gorgeous fashion statement being made at the Moet and Chandon “life in pink” party. The party held at a secret location with the “1 per cent of the 1%”, the creme de la creme of the society, “the real Lagosians” in attendance.
The colour code was pink and black and never has justice been done to that combination as much as the guests did. They slayed in their different outfits and it is totally difficult to give the award of best dressed to any of the attendees.
Below are some of the spectacular and best dressed for the party:
Which is the best dressed to you and which style are you using for your “Detty December Parte After Parte” mood?


--- layout: post title: Firefox hint for the address bar date: '2010-08-09T17:58:00.000-07:00' author: Russell Dickenson tags: - frugalware - linux modified_time: '2012-10-31T21:11:01.948-07:00' blogger_id: tag:blogger.com,1999:blog-510053790932706340.post-2763957054586128594 blogger_orig_url: http://phayzshift.blogspot.com/2010/08/firefox-hint-for-address-bar.html ---
Firefox allows you to assign keyboard shortcuts to search engines, allowing you to conduct searches from within the address bar. Assigning the shortcut "G" to Google for example, allows you to do a Google search in the address bar. If you're searching for references to Halle Berry, you could type 'g "halle berry"' into the address bar.
When I discovered this I was left thinking "Why do I leave the search bar displayed, when I am now using the address bar to search?" To remove it I simply right clicked on the toolbar containing the search bar and from within the dialog, removed the search bar. Voila! I now have a cleaner-looking Firefox without missing any functionality.


Unit 3: Nonlinearity: Log-Transforming the Outcome
In this set of notes, you will learn about log-transforming the outcome variable in a regression model to account for nonlinearity and heterogeneity of variance.
Preparation
Before class you will need to read the following:
- Osborne, Jason (2002). Notes on the use of data transformations. Practical Assessment, Research & Evaluation, 8(6).
3.1 Dataset and Research Question
The data we will use in this set of notes, movies.csv (see the data codebook here), includes attributes for \(n=1,806\) movies.
# Load libraries
library(broom)
library(dplyr)
library(ggplot2)
library(readr)
library(sm)
library(tidyr)
# Import data
movies = read_csv(file = "~/Documents/github/epsy-8252/data/movies.csv")
head(movies)
# A tibble: 6 x 4
title budget age mpaa
<chr> <dbl> <dbl> <chr>
1 'Til There Was You 23 21 PG-13
2 10 Things I Hate About You 16 19 PG-13
3 100 Mile Rule 1.1 16 R
4 13 Going On 30 37 14 PG-13
5 13th Warrior, The 85 19 R
6 15 Minutes 42 17 R
Using these data, we will examine the relationship between age of a movie and budget.
3.2 Examine Relationship between Age and Budget
To being the analysis, we will examine the scatterplot between age and budget of our sample data.
ggplot(data = movies, aes(x = age, y = budget)) +
geom_point() +
geom_smooth(se = FALSE) +
theme_bw() +
xlab("Movie age") +
ylab("Movie Budget (in millions of dollars)")
The scatterplot suggests two potential problems with fitting a linear model to the data:
- The relationship is slightly curvilinear.
- The variation in budget for more recent movies is much greater than the variation in budget for older movies (heteroskedasticity).
We can see this much more clearly in the scatterplot of residuals versus fitted values from a fitted linear model.
# Fit model
lm.1 = lm(budget ~ 1 + age, data = movies)
# Obtain residuals and fitted values
out_1 = augment(lm.1)
# Density plot of the residuals
sm.density(out_1$.std.resid, model = "normal", xlab = "Standardized residuals")
# Residuals versus fitted values
ggplot(data = out_1, aes(x = .fitted, y = .std.resid)) +
geom_point() +
geom_hline(yintercept = 0) +
geom_smooth() +
theme_bw() +
xlab("Fitted values") +
ylab("Standardized residuals")
These plots suggest violations of the normality assumption (the marginal distribution of the residuals is right-skewed) and of the assumption of homoskedasticity. Because of the large sample size, violation the linearity assumption is more difficult to see in this plot.
3.3 Transform the Outcome Using the Natural Logarithm (Base-e)
To alleviate problems of non-normality when the conditional distributions are right-skewed (or have high-end outliers) OR to alleviate heteroskedasticity, we can mathematically transform the outcome using a logarithm. Any base can be used for the logarithm, but we will transform the outcome using the natural logarithm because of the interpretive value.
First, we will create the log-transformed variable as a new column in the data, and then we will use the log-transformed budget (rather than raw budget) in any analyses.
# Create log-transformed budget
movies = movies %>%
mutate(
Lbudget = log(budget)
)
# Examine data
head(movies)
# A tibble: 6 x 5
title budget age mpaa Lbudget
<chr> <dbl> <dbl> <chr> <dbl>
1 'Til There Was You 23 21 PG-13 3.14
2 10 Things I Hate About You 16 19 PG-13 2.77
3 100 Mile Rule 1.1 16 R 0.0953
4 13 Going On 30 37 14 PG-13 3.61
5 13th Warrior, The 85 19 R 4.44
6 15 Minutes 42 17 R 3.74
Recall that the logarithm is the inverse function of an exponent. As an example, consider the budget and log-transformed budget for ’Til There Was You.
\[ \begin{split} \ln(\textrm{Budget}) &= 3.135 \\ \ln(23.0) &= 3.135 \\ \end{split} \]
Or,
\[ e^{3.135} = 23.0 \]
Remember, the logarithm answers the mathematical question:
\(e\) to what power is equal to 23.0?
3.4 Re-analyze using the Log-Transformed Budget
Now we will re-examine the scatterplot using the log-transformed outcome to see how this transformation affects the relationship.
# Scatterplot
ggplot(data = movies, aes(x = age, y = Lbudget)) +
geom_point() +
geom_smooth(se = FALSE) +
theme_bw() +
xlab("Movie age") +
ylab("ln(Movie Budget)")
Log-transforming the outcome has drastically affected the scale for the outcome. Has this helped us better meet the assumptions? Again, we should examine the residual plots.
# Fit model
lm.2 = lm(Lbudget ~ 1 + age, data = movies)
# Obtain residuals and fitted values
out_2 = augment(lm.2)
# Density plot of the residuals
sm.density(out_2$.std.resid, model = "normal", xlab = "Standardized residuals")
# Residuals versus fitted values
ggplot(data = out_2, aes(x = .fitted, y = .std.resid)) +
geom_point() +
geom_hline(yintercept = 0) +
geom_smooth() +
theme_bw() +
xlab("Fitted values") +
ylab("Standardized residuals")
These plots still suggest violations of the normality assumption (the marginal distribution of the residuals is now left-skewed). The assumption of homoskedasticity also seems still violated, although much less. Most importantly, however, is the assumption of linearity now seems satisfied.
3.5 Interpreting the Regression Output
Let’s examine the output from the model in which we regressed the log-transformed budget on age.
# Model-level output
glance(lm.2)
# A tibble: 1 x 11
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC
<dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
1 0.0213 0.0208 1.74 39.3 4.60e-10 2 -3560. 7125. 7142.
# … with 2 more variables: deviance <dbl>, df.residual <int>
The model-level summary information suggests that differences in movies’ ages explains 2.1% of the variation in budget. (Remember, explaining variation in log-budget is the same as explaining variation in budget). Although this is a small amount of variation, it is statistically significant, \(F(1,1804)=39.27\), \(p<0.001\).
# Coefficient-level output
tidy(lm.2)
# A tibble: 2 x 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.28 0.139 23.6 1.69e-107
2 age -0.0441 0.00703 -6.27 4.60e- 10
From the coefficient-level output the fitted equation is:
\[ \ln\left(\hat{\mathrm{Budget}_i}\right) = 3.28 - 0.04(\mathrm{Age}_i) \]
With log-transformations, there are two possible interpretations we can offer. The first is to interpret the coefficients using the log-transformed values. These we interpret in the exact same way we do any other regression coefficients (except we use log-outcome instead of outcome):
- The intercept, \(\hat{\beta_0} = 3.28\), is the average predicted log-budget for movies made in 2019 (Age = 0).
- The slope, \(\hat{\beta_1} = -0.04\), indicates that each one-year difference in age is associated with a log-budget that differ by \(-0.04\), on average.
3.5.1 Back-Transforming: A More Useful Interpretation
A second, probably more useful, interpretation is to back-transform log-budget to budget. To think about how to do this, we first consider a more general expression of the fitted linear model:
\[ \ln\left(\hat{Y}_i\right) = \hat\beta_0 + \hat\beta_1(X_{i}) \]
The left-hand side of the equation is in the log-transformed metric, which drives our interpretations. If we want to instead, interpret using the raw metric of \(Y\), we need to back-transform from \(\ln(Y)\) to \(Y\). To back-transform, we use the inverse function, which is to exponentiate using the base of the logarithm, in our case, base-\(e\).
\[ e^{\ln(Y_i)} = Y_i \]
If we exponentiate the left-hand side of the equation, to maintain the equality, we also need to exponentiate the right-hand side of the equation.
\[ e^{\ln(Y_i)} = e^{\hat\beta_0 + \hat\beta_1(X_{i})} \]
Then we use rules of exponents to simplify this.
\[ Y_i = e^{\hat\beta_0} \times e^{\hat\beta_1(X_{i})} \]
For our example, when we exponentiate both sides of the fitted equation:
\[ \hat{\mathrm{Budget}_i} = e^{3.28} \times e^{-0.04(\mathrm{Age}_i)} \]
3.5.2 Substituting in Values for Age to Interpret Effects
To interpret the effects (which are now interpreted using budget—not log-budget) we can substitute in the different values for age and solve. For example when Age = 0:
\[ \begin{split} \hat{\mathrm{Budget}_i} &= e^{3.28} \times e^{-0.04(0)}\\ &= 26.58 \times 1 \\ &= 26.58 \end{split} \]
The predicted budget for a movie made in 2019 is 26.58 million dollars. How about a movie that was made in 2018 (a one-year difference)?
\[ \begin{split} \hat{\mathrm{Budget}_i} &= e^{3.28} \times e^{-0.04(1)}\\ &= 26.58 \times 0.96 \\ &= 25.54 \end{split} \]
The predicted budget for a movie made in 2019 is 25.54 million dollars. This is 0.96 TIMES the budget of a movie made in 2018.
Rather than using the language of TIMES difference you could also use the language of fold difference. In this case the slope coefficient would be interpreted as,
Each one-year difference in age is associated with a 0.95-fold difference in budget, on average.
Simply put, when we back-transform from interpretations of log-\(Y\) to \(Y\) the interpretations are multiplicatively related to the intercept rather than additive. We can obtain these multiplicative values (and the back-transformed intercept) by using the exp()
function to exponentiate the coefficients from the fitted model, which we obtain using the coef()
function.
exp(coef(lm.2))
(Intercept) age
26.5261 0.9569
3.5.3 Approximate Interpretation of the Slope
Remember that by using the natural logarithm we can interpret the effects as percent change. Rather than saying that a movie made in 2018 is predicted to have a budget that is 0.96 TIMES that of a movie made in 2019, we can directly interpret the slope as the percent change. Thus \(\hat{\beta_1}=-0.04\) can be interpreted as:
Each one-year difference in age is associated with a four percent decrease in budget, on average.
If you want the specific mathematical change in budget, find \(1 - e^{\hat{\beta_1}}\).
1 - exp(-0.04)
[1] 0.03921
If you use the language of percent decrease/increase, be very careful. Percent change and percentage change are sometimes interpreted differently! It is generally more clear to use the X-fold difference language.
3.6 Plotting the Fitted Model
As always, we can plot the fitted model to aid in interpretation. To do this we will create a sequence of ages, predict the log-budget using the fitted model, and then back-transform the log-budgets to raw budget.
# Set up data
plot_data = crossing(
age = seq(from = 13, to = 80, by = 1)
) %>%
mutate(
# Predict
yhat = predict(lm.2, newdata = .)
)
# Examine data
head(plot_data)
# A tibble: 6 x 2
age yhat
<dbl> <dbl>
1 13 2.71
2 14 2.66
3 15 2.62
4 16 2.57
5 17 2.53
6 18 2.48
# Back-transform the log-budgets
plot_data = plot_data %>%
mutate(
budget = exp(yhat)
)
# Examine data
head(plot_data)
# A tibble: 6 x 3
age yhat budget
<dbl> <dbl> <dbl>
1 13 2.71 15.0
2 14 2.66 14.3
3 15 2.62 13.7
4 16 2.57 13.1
5 17 2.53 12.5
6 18 2.48 12.0
# Plot
ggplot(data = plot_data, aes(x = age, y = budget)) +
geom_line() +
theme_bw() +
xlab("Age") +
ylab("Predicted budget (in millions of U.S. dollars)")
Based on this plot, we see the non-linear, negative effect of age on budget. In other words, older movies tend to have a smaller budget, on average, but this decrease is not constant. This pattern of non-linear decline is referred to as exponential decay.
Although this function has a different look than the function we saw in the previous unit (it is negative rather than positive), it is also a monotonic function (no change in direction).
3.7 Relationship between MPAA Rating and Budget
We also may want to control for differences in MPAA rating. Before we fit the multiple regression model, however, we will first explore whether MPAA rating is a useful covariate by seeing whether there are differences in budget between PG, PG-13m and R rated movies. Since we log-transformed budget (the outcome) in the previous analysis we will need to use the log-transformed outcome in this exploration as well.
# Plot the observed data
ggplot(data = movies, aes(x = mpaa, y = Lbudget)) +
geom_jitter(alpha = 0.2) +
stat_summary(fun.y = 'mean', geom = "point", size = 4, color = "darkred") +
theme_bw() +
xlab("MPAA rating") +
ylab("ln(Movie Budget)")
# Compute summary statistics
movies %>%
group_by(mpaa) %>%
summarize(
M = mean(Lbudget),
SD = sd(Lbudget)
)
# A tibble: 3 x 3
mpaa M SD
<chr> <dbl> <dbl>
1 PG 2.88 1.53
2 PG-13 3.14 1.51
3 R 2.01 1.78
The scatterplot and summary statistics indicate there are sample differences in the mean log-budgets for the three MPAA ratings. The variation in log-budgets seems roughly the same for the three ratings.
3.7.1 Regression Model
Let’s regress log-transformed budget on MPAA rating and examine the output from the model. To do so, we will need to first create three dummy variables for the different ratings.
# Create dummy variables
movies = movies %>%
mutate(
pg = if_else(mpaa == "PG", 1, 0),
pg13 = if_else(mpaa == "PG-13", 1, 0),
r = if_else(mpaa == "R", 1, 0)
)
# Fit the model (pg is reference group)
lm.3 = lm(Lbudget ~ 1 + pg13 + r, data = movies)
# Model-level output
glance(lm.3)
# A tibble: 1 x 11
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC
<dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
1 0.0892 0.0882 1.68 88.3 2.66e-37 3 -3495. 6997. 7019.
# … with 2 more variables: deviance <dbl>, df.residual <int>
The model-level summary information suggests that differences in MPAA rating explains 8.9% of the variation in budget. (Remember, explaining variation in log-budget is the same as explaining variation in budget). Although this is a small amount of variation, it is statistically significant, \(F(2,1803)=88.28\), \(p<0.001\).
# Coefficient-level output
tidy(lm.3)
# A tibble: 3 x 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 2.88 0.115 25.0 9.98e-119
2 pg13 0.262 0.136 1.92 5.45e- 2
3 r -0.867 0.126 -6.88 8.49e- 12
From the coefficient-level output we see that the fitted equation is:
\[ \ln\left(\hat{\mathrm{Budget}_i}\right) = 2.88 + 0.26(\mathrm{PG\mbox{-}13}_i) - 0.87(\mathrm{R}_i) \]
With log-transformations, there are two possible interpretations we can offer. The first is to interpret the coefficients using the log-transformed values. These we interpret in the exact same way we do any other regression coefficients (except we use log-outcome instead of outcome):
- The intercept, \(\hat{\beta_0} = 2.88\), is the average predicted log-budget for PG rated movies.
- The slope associated with PG-13, \(\hat{\beta_1} = 0.26\), indicates that PG-13 rated movies have a log-budget that is 0.26 higher than PG rated movies, on average.
- The slope associated with R, \(\hat{\beta_2} = -0.87\), indicates that R rated movies have a log-budget that is 0.87 lower than PG rated movies, on average.
We can also interpret these by back-transforming to raw budget. To do that we exponentiate the coefficients.
exp(coef(lm.3))
(Intercept) pg13 r
17.8134 1.2998 0.4201
- PG rated movies have a budget of 17.81 million dollars, on average.
- PG-13 rated movies have a budget that is 1.30 TIMES the estimated budget for PG rated movies, on average.
- R rated movies have a budget that is 0.42 TIMES the estimated budget for PG rated movies, on average.
3.7.2 Mathematical Explanation
Remember, if we want to interpret using the raw metric of \(Y\), we need to back-transform from \(\ln(Y)\) to \(Y\). To back-transform, we use the inverse function, which is to exponentiate using the base of the logarithm, in our case, base-\(e\). For our example, when we exponentiate both sides of the fitted equation:
\[ \begin{split} e^{\ln\left(\hat{\mathrm{Budget}_i}\right)} &= e^{2.88 + 0.26(\mathrm{PG\mbox{-}13}_i) - 0.87(\mathrm{R}_i)} \\ \hat{\mathrm{Budget}_i} &= e^{2.88} \times e^{0.26(\mathrm{PG\mbox{-}13}_i)} \times e^{-0.87(\mathrm{R}_i)} \end{split} \]
To interpret the effects (which are now interpreted using budget—not log-budget) we can substitute in the different dummy variable patterns and solve.
\[ \begin{split} \textbf{PG Movie:}~~ \hat{\mathrm{Budget}_i} &= e^{2.88} \times e^{0.26(0)} \times e^{-0.87(0)}\\ &= 17.81 \times 1 \times 1 \\ &= 17.81 \end{split} \]
\[ \begin{split} \textbf{PG-13 Movie:}~~ \hat{\mathrm{Budget}_i} &= e^{2.88} \times e^{0.26(1)} \times e^{-0.87(0)}\\ &= 17.81 \times 1.30 \times 1 \\ &= 23.15 \end{split} \]
\[ \begin{split} \textbf{R Movie:}~~ \hat{\mathrm{Budget}_i} &= e^{2.88} \times e^{0.26(0)} \times e^{-0.87(1)}\\ &= 17.81 \times 1 \times 0.42 \\ &= 7.48 \end{split} \]
3.7.3 Approximate Interpretations
Unfortunately, the approximate interpretations of the slopes by directly interpreting the coefficients using the language of percent change are not completely trustworthy. If we did interpret them, the interpretations for the two slopes would be:
- PG-13 rated movies have budget that is 26.2 percent higher than PG rated movies, on average.
- R rated movies have budget that is 87 percent lower than PG rated movies, on average.
This interpretation is roughly true for the PG-13 effect, but not for the R effect. This approximate interpretation starts to become untrustworthy when the slope value is higher than about 0.20 or so.
3.8 Multiple Regression: Main Effects Model
Now we can fit a model that includes our focal predictor of age and our covariate of MPAA rating.
# Fit model (PG is reference group)
lm.4 = lm(Lbudget ~ 1 + age + pg13 + r, data = movies)
# Model-level output
glance(lm.4)
# A tibble: 1 x 11
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC
<dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
1 0.109 0.108 1.66 73.8 4.82e-45 4 -3474. 6959. 6986.
# … with 2 more variables: deviance <dbl>, df.residual <int>
The model-level summary information suggests that differences in age and MPAA rating of a movie explains 11.0% of the variation in budget. (Remember, explaining variation in log-budget is the same as explaining variation in budget); \(F(3,1802)=73.84\), \(p<0.001\).
# Coefficient-level output
tidy(lm.4)
# A tibble: 4 x 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.73 0.175 21.3 3.76e-90
2 age -0.0431 0.00673 -6.41 1.89e-10
3 pg13 0.208 0.135 1.54 1.23e- 1
4 r -0.904 0.125 -7.24 6.79e-13
The coefficient-level output suggest that there is still a statistically significant effect of age on budget, after controlling for differences in MPAA rating; \(t(1802)=-6.41\), \(p<.001\).
To determine if there is an effect of MPAA rating, after accounting for differences in age, at least one of the effects of MPAA rating need to be statistically significant. Here we see that the coefficient associated with rated R movies is statistically significant.
Remember that when we have more than two categories (more than one dummy variable) there can be many ways for the effect to play out, and not all of these are represented in the model we fitted. One way we can simultaneously examine ALL the ways this effect can play out is to use a nested \(F\)-test.
3.8.1 Nested F-Test
If we want to examine if there is a controlled effect of MPAA rating (controlling for age), we want to see whether by including MPAA rating in a model THAT ALREADY INCLUDES age we explain additional variation in the outcome. To do this we can compare a model that only includes the effect of age to a model that includes both the effects of age and MPAA rating. If the latter model explains a statistically significant amount of additional variation we can say that there is an effect of MPAA rating after controlling for differences in age.
In statistical hypothesis testing we are examining the following null hypothesis:
\[ H_0: \rho^2_{\mathrm{Age},\mathrm{MPAA~rating}} - \rho^2_{\mathrm{Age}} = 0 \]
If we fail to reject this hypothesis, then the two models explain the SAME amount of variation and we should adopt the simpler model; MPAA rating does not explain additional variation in budget. If we reject this hypothesis, MPAA rating does explain additional variation in budget, above and beyond age; and we should adopt the model that includes both effects.
To test this hypothesis we fit both models and then give both models to the anova()
function.
# Fit models
lm.2 = lm(Lbudget ~ 1 + age, data = movies)
lm.4 = lm(Lbudget ~ 1 + age + pg13 + r, data = movies)
# Nested F-test
anova(lm.2, lm.4)
Analysis of Variance Table
Model 1: Lbudget ~ 1 + age
Model 2: Lbudget ~ 1 + age + pg13 + r
Res.Df RSS Df Sum of Sq F Pr(>F)
1 1804 5448
2 1802 4957 2 491 89.2 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
The test suggests that there is a statistically significant effect of MPAA rating even after accounting for differences in age; \(F(2, 1802)=89.21\), \(p<.001\).
3.8.2 Coefficient-Level Interpretation
To interpret the coefficients, we will again exponentiate the fitted coefficients so we can interpret them using the raw-metric of budget.
exp(coef(lm.4))
(Intercept) age pg13 r
41.7120 0.9578 1.2318 0.4051
- The model estimated budget for a PG movie (reference group) that was made in 2019 (age = 0) is 41.71 million dollars.
- Each one-year difference in age is associated with a 0.96-fold difference (4.3% decrease) in budget, on average, controlling for differences in MPAA rating.
- PG-13 rated movies have a budget that is 1.23 times that for PG movies, on average, controlling for differences in age.
- R rated movies have a budget that is 0.41 times that for PG movies, on average, controlling for differences in age.
3.8.3 Plot of the Fitted Model
To plot the fitted model that includes a categorical predictor with more than two levels, it is best to re-fit the lm()
using the categorical variable.
# Re-fit the model
lm.5 = lm(Lbudget ~ 1 + age + mpaa, data = movies)
# Model-level output
glance(lm.5)
# A tibble: 1 x 11
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC
<dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
1 0.109 0.108 1.66 73.8 4.82e-45 4 -3474. 6959. 6986.
# … with 2 more variables: deviance <dbl>, df.residual <int>
# Coefficient-level output
tidy(lm.5)
# A tibble: 4 x 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.73 0.175 21.3 3.76e-90
2 age -0.0431 0.00673 -6.41 1.89e-10
3 mpaaPG-13 0.208 0.135 1.54 1.23e- 1
4 mpaaR -0.904 0.125 -7.24 6.79e-13
Note this is the exact same model we fitted using the dummy variables, but R will choose the reference group for us (alphabetically). We can now set up our plotting data, predict, back-transform the outcome, and plot.
# Set up data
plot_data = crossing(
age = seq(from = 13, to = 80, by = 1),
mpaa = c("PG", "PG-13", "R")
) %>%
mutate(
yhat = predict(lm.5, newdata = .),
budget = exp(yhat)
)
# Examine data
head(plot_data)
# A tibble: 6 x 4
age mpaa yhat budget
<dbl> <chr> <dbl> <dbl>
1 13 PG 3.17 23.8
2 13 PG-13 3.38 29.3
3 13 R 2.27 9.65
4 14 PG 3.13 22.8
5 14 PG-13 3.34 28.1
6 14 R 2.22 9.24
# Plot
ggplot(data = plot_data, aes(x = age, y = budget, color = mpaa, linetype = mpaa)) +
geom_line() +
theme_bw() +
xlab("Age") +
ylab("Predicted budget (in millions of U.S. dollars)") +
ggsci::scale_color_d3(name = "MPAA rating") +
scale_linetype_manual(name = "MPAA rating", values = 1:3)
The plot displays the negative, nonlinear effect of age on budget for all three types of movies (main effect of age). It also shows that PG-13 rated movies have a higher predicted budget than PG and R rated movies, and that PG rated movies have a higher predicted budget than R rated movies at EVERY age. This is the main effect of MPAA rating.
Notice that in the plot, the three lines are not parallel. This is a mathematical artifact of back-transforming log-budget to raw budget. It does not indicate that an interaction model was fitted. How non-parallel the lines are depends on the size of the coefficients associated with the MPAA effects (in this example). This is why, especially with transformed data, it is essential to plot the model to make sure you are understanding the interpretations from your coefficients.
3.9 Multiple Regression: Interaction Model
To study whether there is an interaction effect between MPAA rating and age, we will fit the interaction model and compare it to the main-effects model using the nested \(F\)-test.
# Fit the models
lm.5 = lm(Lbudget ~ 1 + age + mpaa, data = movies)
lm.6 = lm(Lbudget ~ 1 + age + mpaa + age:mpaa, data = movies)
# Nested F-test
anova(lm.5, lm.6)
Analysis of Variance Table
Model 1: Lbudget ~ 1 + age + mpaa
Model 2: Lbudget ~ 1 + age + mpaa + age:mpaa
Res.Df RSS Df Sum of Sq F Pr(>F)
1 1802 4957
2 1800 4949 2 7.76 1.41 0.24
The test suggests that we should adopt the main-effects model. The interaction-effect was not statistically significant; \(F(2,1800)=1.41\), \(p=.244\).
If the model that included the interaction effect was adopted, it would suggest that: (1) the effect of age on budget depends on MPAA rating, or (2) the effect of MPAA rating on budget depends on age of the movie. To further interpret these effects, you should plot the results of the fitted interaction model.


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Mobile title Romancing SaGa Re;universe headed west in 2020
Century-spanning pocket adventures
by Chris MoyseThe hugely popular Japanese mobile title Romancing SaGa Re;universe will be headed west next year, according to publisher Square Enix. A teaser trailer was released for the spin-off release in the legendary Romancing SaGa sub-series, which began on the Super Famicom way back in 1992.
Already downloaded over 15 million times in Japan, Romancing SaGa Re;universe follows on from the events of Romancing SaGa 3 (albeit some 300 years later), and includes appearances from a huge cast of characters taken from previous SaGa titles. You can check out the trailer for the Japanese edition below.
Currently there is no strict release window for Romancing SaGa Re;universe's arrival, but it is expected at some point in 2020 for both iOS and Android devices. The most recent entry in the series, Romancing SaGa 3, is currently available on PS4, PS Vita, PC, Xbox One, Nintendo Switch and mobile devices.
Romancing SaGA Re;universe coming west in 2020 [Gematsu]